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» Approximation algorithms for budgeted learning problems
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ATAL
2009
Springer
15 years 4 months ago
An empirical analysis of value function-based and policy search reinforcement learning
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
Shivaram Kalyanakrishnan, Peter Stone
CEAS
2006
Springer
15 years 1 months ago
Fast Uncertainty Sampling for Labeling Large E-mail Corpora
One of the biggest challenges in building effective anti-spam solutions is designing systems to defend against the everevolving bag of tricks spammers use to defeat them. Because ...
Richard Segal, Ted Markowitz, William Arnold
IJON
2006
131views more  IJON 2006»
14 years 9 months ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...
GECCO
2006
Springer
195views Optimization» more  GECCO 2006»
15 years 1 months ago
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Recently, studies with the XCS classifier system on Boolean functions have shown that in certain types of functions simple crossover operators can lead to disruption and, conseque...
Martin V. Butz, Martin Pelikan
ICML
2009
IEEE
15 years 10 months ago
Binary action search for learning continuous-action control policies
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
Jason Pazis, Michail G. Lagoudakis